# -*- coding: utf-8 -*-
"""
The core fluid solver module.

This module contains the placeholder classes and functions for the main CFD solver.
It is structured according to the "Coupled Solver Module" section of the report.

**THIS IS A PLACEHOLDER MODULE.** The core logic for solving the Navier-Stokes
equations is not implemented. It serves as a structural guide for where the
numerical methods (discretization, linear system assembly, solving) should be
implemented.
"""
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as spla
import logging

from .mesh import Mesh

class CoupledSolver:
    """
    A placeholder for a pressure-based coupled CFD solver.
    
    This solver is intended to solve the momentum and continuity equations
    simultaneously. The implementation details are left as an exercise for
    the user.
    """
    def __init__(self, mesh: Mesh, config):
        """
        Initializes the solver.

        Args:
            mesh (Mesh): The computational mesh object.
            config: The simulation configuration object.
        """
        self.mesh = mesh
        self.config = config
        
        # Initialize flow fields. For a 3D simulation:
        # u, v, w velocities and pressure p for each node.
        num_nodes = self.mesh.points.shape[0]
        self.velocity = np.zeros((num_nodes, 3))
        self.pressure = np.zeros(num_nodes)
        
        # Set initial conditions (e.g., freestream velocity)
        self.velocity[:, 0] = self.config.INLET_VELOCITY
        
        # Initialize residual tracking for stable placeholder behavior
        self.initial_pressure_residual = 1e-2
        self.initial_velocity_residual = 1e-5
        self.current_iteration = 0
        self.time_step_count = 0
        
        logging.info("CoupledSolver initialized. Flow fields created.")
        logging.warning("Solver logic is not implemented. This is a placeholder.")

    def solve_for_timestep(self) -> bool:
        """
        Executes the solver for a single time step.

        This involves an inner loop of iterations to converge the non-linear system.
        
        Returns:
            bool: True if the solution converged, False otherwise.
        """
        logging.debug("Executing solver for one time step...")
        
        # Reset iteration counter for this time step
        self.current_iteration = 0
        self.time_step_count += 1
        
        for i in range(self.config.INNER_ITERATIONS):
            self.current_iteration = i + 1
            
            # The core logic of a coupled solver would be here.
            
            # 1. Assemble the global system of equations [A][x] = [b]
            A, b = self._assemble_system()
            
            # 2. Solve the linear system for the solution vector [x]
            # The solution vector x contains the corrections for u, v, w, p.
            x, exit_code = self._solve_linear_system(A, b)
            
            if exit_code != 0:
                logging.error(f"Linear solver failed with exit code {exit_code}.")
                return False
            
            # 3. Update the flow fields (velocity and pressure) with the corrections.
            self._update_fields(x)
            
            # 4. Calculate residuals to check for convergence.
            residuals = self._calculate_residuals()
            
            # Only log at debug level during iterations
            logging.debug(f"  - Iteration {i+1}, Residuals: pressure={residuals['pressure']:.2e}, velocity={residuals['velocity']:.2e}")
            
            # 5. Check if convergence criteria are met.
            if self._check_convergence(residuals):
                logging.debug(f"Converged in {i+1} iterations.")
                return True
        
        logging.warning("Solver did not converge within the specified number of inner iterations.")
        logging.warning(f"Final residuals: pressure={residuals['pressure']:.2e}, velocity={residuals['velocity']:.2e}")
        return False

    def _assemble_system(self):
        """
        (Placeholder) Assembles the sparse linear system [A][x] = [b].
        
        This is the most complex part of a CFD solver. It involves iterating
        through every cell in the mesh and calculating the coefficients of the
        discretized Navier-Stokes equations.
        
        - The temporal term (First-Order Implicit) is discretized.
        - The convective term (e.g., using QUICK scheme) is discretized.
        - The pressure gradient and viscous terms are discretized.
        
        The matrix [A] and vector [b] would be constructed here.
        """
        logging.debug("Assembling linear system (placeholder)...")
        num_vars = 4 # u, v, w, p
        num_nodes = self.mesh.points.shape[0]
        system_size = num_vars * num_nodes
        
        # Create a dummy sparse matrix and vector for demonstration.
        # In a real solver, these would be populated with coefficients from the PDEs.
        A = sp.lil_matrix((system_size, system_size))
        b = np.zeros(system_size)
        
        # A real implementation would loop over cells and faces to fill A and b.
        # For the placeholder, create a more realistic system that ensures convergence
        # Make it diagonally dominant with some off-diagonal coupling
        diagonal_value = 1.0 + 0.1 * self.current_iteration  # Increasing diagonal dominance
        A.setdiag(diagonal_value)
        
        # Add some off-diagonal terms to simulate coupling
        # This makes the system more realistic while still being solvable
        for i in range(min(1000, system_size - 1)):  # Limited coupling for performance
            if i + 1 < system_size:
                A[i, i + 1] = -0.01
                A[i + 1, i] = -0.01
        
        # Create a non-zero RHS that decreases with iterations
        b[:] = 0.1 / (1.0 + 0.5 * self.current_iteration)
        
        return A.tocsr(), b

    def _solve_linear_system(self, A, b):
        """
        (Placeholder) Solves the assembled linear system.
        
        Uses an iterative solver from SciPy's sparse linear algebra library.
        """
        logging.debug("Solving linear system (placeholder)...")
        # GMRES (Generalized Minimal Residual) is a good choice for non-symmetric systems.
        # The 'tol' argument was renamed to 'rtol' (relative tolerance) in newer
        # versions of SciPy.
        x, exit_code = spla.gmres(A, b, rtol=1e-8, maxiter=100)
        return x, exit_code

    def _update_fields(self, corrections):
        """
        (Placeholder) Updates the velocity and pressure fields with the solution.
        """
        logging.debug("Updating flow fields (placeholder)...")
        # The 'corrections' vector would be unpacked and added to the
        # velocity and pressure fields.
        # For the placeholder, we simulate small changes to the fields
        num_nodes = self.mesh.points.shape[0]
        
        # Apply small corrections that decrease with iterations
        correction_scale = 0.01 / (1.0 + self.current_iteration)
        self.velocity += correction_scale * np.random.randn(num_nodes, 3) * 0.1
        self.pressure += correction_scale * np.random.randn(num_nodes) * 0.01

    def _calculate_residuals(self) -> dict:
        """
        (Placeholder) Calculates the residuals of the governing equations.
        
        Residuals measure how well the current solution satisfies the equations.
        This function would compute the L2 norm of the imbalance for each equation.
        """
        # For stable placeholder behavior, make residuals decrease with iterations
        # This ensures eventual convergence while still simulating the iterative nature
        
        # Use an exponential decay based on iteration count
        decay_factor = np.exp(-0.3 * self.current_iteration)
        
        # Add some variation to make it more realistic
        # but ensure it trends downward
        noise_factor = 1.0 + 0.1 * np.sin(self.current_iteration)
        
        pressure_residual = self.initial_pressure_residual * decay_factor * noise_factor
        velocity_residual = self.initial_velocity_residual * decay_factor * noise_factor
        
        # Ensure residuals don't increase
        pressure_residual = min(pressure_residual, self.initial_pressure_residual)
        velocity_residual = min(velocity_residual, self.initial_velocity_residual)
        
        return {
            'pressure': max(pressure_residual, 1e-10),  # Prevent zero/negative
            'velocity': max(velocity_residual, 1e-12)   # Prevent zero/negative
        }

    def _check_convergence(self, residuals: dict) -> bool:
        """
        Checks if the solution has converged based on the residuals.
        """
        pres_res = residuals['pressure'] < self.config.PRESSURE_RESIDUAL
        vel_res = residuals['velocity'] < self.config.VELOCITY_RESIDUAL
        return pres_res and vel_res 